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Updated: Apr 24, 2026
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The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task
Published on: May 3, 2018
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Motor variability arises from a slow random walk in neural state.
Kris S Chaisanguanthum1, Helen H Shen2, Philip N Sabes3
1Center for Integrative Neuroscience and Department of Physiology and Sloan-Swartz Center for Theoretical Neurobiology, University of California, San Francisco, San Francisco, California 94143.
Summary
Motor variability in movements, like pitching or reaching, stems from a drifting average and rapid fluctuations. These drifts, observed in both humans and monkeys, are linked to motor learning processes.
Area of Science:
- Neuroscience
- Motor Control
- Biomechanics
Background:
- Movement variability is traditionally attributed to random noise in motor preparation and execution.
- Understanding the sources of this variability is crucial for fields ranging from robotics to rehabilitation.
Purpose of the Study:
- To investigate the statistical structure of motor variability in human and non-human primates.
- To determine the relationship between neural activity and behavioral variability during motor tasks.
- To explore the potential role of motor learning in shaping movement variability.
Main Methods:
- Analysis of movement data from professional baseball pitchers and macaque monkeys performing reaching tasks.
- Recording of neural activity from dorsal premotor cortex/primary motor cortex in monkeys.
- Statistical decomposition of motor variability into mean drift and trial-by-trial fluctuations.
- Modeling of neural and behavioral drift using autocorrelation functions.
Main Results:
- Motor variability can be separated into a slowly drifting mean and fast trial-by-trial fluctuations.
- Neural activity in premotor cortex exhibits similar drift statistics to behavior.
- Neural drifts correlate with behavioral drifts, but neural activity does not explain trial-by-trial fluctuations.
- Drift statistics are well-modeled by a double-exponential autocorrelation function with time constants linked to motor learning.
Conclusions:
- Motor variability arises not just from random noise but from longer-timescale processes, potentially related to motor learning.
- Error-corrective learning mechanisms may explain the observed drift dynamics in motor control.
- While neural and behavioral drifts are linked, downstream processes likely contribute to rapid movement fluctuations.
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